{"id":31039,"date":"2023-02-27T14:06:20","date_gmt":"2023-02-27T22:06:20","guid":{"rendered":"https:\/\/sop.washington.edu\/?page_id=31039"},"modified":"2023-02-27T14:08:43","modified_gmt":"2023-02-27T22:08:43","slug":"heor-data-science-degree-option","status":"publish","type":"page","link":"https:\/\/staff.washington.edu\/twentzel\/wordpress\/choice\/graduate-education-training-programs\/phd-program\/heor-data-science-degree-option\/","title":{"rendered":"HEOR Data Science Degree Option"},"content":{"rendered":"

The CHOICE Institute is now offering currently enrolled PhD students Health Economics and Outcomes Research (HEOR) the opportunity to pursue a specialization in Data Science (DSO). Students will explore the fundamentals in computation, machine learning, visual techniques, study designs, and statistics to address quantitative challenges in HEOR. They will develop the latest data analysis skills essential to advance research and improve the efficiency of decision-making processes.\u00a0 The option is based on a framework developed by the University of Washington eScience Institute<\/a>.<\/p>\n

Curriculum:<\/h3>\n

The HEOR Degree Option is spread across the following five domains, including four courses:<\/p>\n

    \n
  1. Programming Skills\/Software Engineering<\/li>\n
  2. Machine Learning<\/li>\n
  3. Data Visualization<\/li>\n
  4. Capstone in HEOR<\/li>\n
  5. Study Design\/Biostatistics\/Data analysis<\/li>\n<\/ol>\n

    Requirements:<\/h3>\n

    This specialization requires the completion of 14 credits that comprise of the following mandatory courses and their domains:<\/p>\n

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    \n

    Required Courses for HEOR Degree Option in Data Science<\/h3>\n<\/td>\n<\/tr>\n

    Domain<\/strong><\/td>\nCourse #, Name, & Credits<\/strong><\/td>\nDescription<\/strong><\/td>\n<\/tr>\n
    Programming Skills\/ Software Engineering<\/td>\n*CSE 583<\/strong>: Software Development for Data Scientists<\/p>\n

    (4 CR)\u00a0<\/em><\/p>\n

    For non-CSE majors\u00a0<\/em><\/td>\n

    		Provides a foundational understanding of\u00a0computation and practical data analysis. I am learning\u00a0to code in Python to solve real-world scientific\u00a0problems.<\/td>\n<\/tr>\n
    Machine Learning<\/td>\nBIOST 546:<\/strong> Machine Learning for Biomedical and Public\u00a0Health Big Data<\/p>\n

    (3 CR)\u00a0<\/em><\/p>\n

     <\/td>\n

    		Fundamentals of machine learning (supervised and\u00a0unsupervised); bias\/variance trade-off; cross validation; classification techniques; Taught in R.<\/td>\n<\/tr>\n
    Data Visualization<\/td>\nCSE 512:<\/strong>\u00a0 Data Visualization<\/p>\n

    (4 CR)\u00a0<\/em><\/p>\n

    OR\u00a0<\/strong><\/p>\n

    CSE 412:<\/strong> Introduction to Data Visualization<\/p>\n

    (4 CR)\u00a0<\/em><\/td>\n

    		Explores visual techniques for investigating\u00a0patterns in data, summarizing, and efficiently\u00a0presenting statistical results. Taught in R.<\/td>\n<\/tr>\n
    Capstone in HEOR<\/td>\nHEOR 552:<\/strong> Application of Machine Learning HEOR (3 CR)<\/em><\/td>\nThis course will integrate the above material and\u00a0apply it to HEOR.<\/td>\n<\/tr>\n
    BIOST = Biostatistics; CSE = Computer Science & Engineering; HEOR = Health Economics and Outcomes Research\u00a0<\/em><\/p>\n

    *CSE 160: Fundamentals of Programming may be taken prior to CSE 583 but is an undergraduate course and no credit will be awarded.<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

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    \n

    Suggestions for (Optional) Additional Courses<\/h3>\n<\/td>\n<\/tr>\n

    Domain<\/strong><\/td>\nCourse #, Name, & Credits<\/strong><\/td>\nDescription<\/strong><\/td>\n<\/tr>\n
    Study Design,\u00a0\u00a0<\/strong><\/p>\n

    Biostatistics and Data\u00a0Analysis<\/strong><\/td>\n

    		Highly recommended: BIOST 540<\/strong><\/p>\n

    Option: BIOST 536, others; HSERV 524<\/strong><\/p>\n

    BIOST 544:<\/strong> Intro to Biomedical Data Science<\/p>\n

    (4 CR; optional)\u00a0<\/em><\/td>\n

    		Fundamentals of study designs; fundamentals of\u00a0epidemiology; fundamentals of probability and\u00a0statistics; learning in detail about model functional\u00a0forms and assumptions; designing studies to\u00a0answer specific scientific questions and selecting\u00a0functional forms to answer those questions most\u00a0appropriately.<\/p>\n

    BIOSTAT 544 covers four critical aspects of data\u00a0science: 1) data acquisition\/common data models, 2) data transformation and integration (wrangling), 3) interactive and dynamic exploratory data\u00a0analysis, 4) data visualization analytics. Taught in\u00a0R.<\/td>\n<\/tr>\n

    Data Science Seminar\u00a0<\/strong><\/td>\nCHEME 599<\/strong>: Topics in Data Science<\/p>\n

    (1-3 CR)\u00a0<\/em><\/td>\n

    		Provides broad overview and discussion of issues\u00a0across scientific disciplines<\/td>\n<\/tr>\n
    Research Ethics in the<\/strong>\u00a0Data Sciences<\/strong><\/td>\nBIOSTS 532: <\/strong>Research Ethics in the Data Sciences<\/p>\n

    (2 CR)\u00a0<\/em><\/td>\n

    		Explores ethical issues related to the computation, interpretation and communication of statistics, and\u00a0provides students with knowledge and resources to\u00a0practice statistics ethically.<\/td>\n<\/tr>\n
    Database Management\u00a0<\/strong><\/td>\nCSE 544:<\/strong> Principles of Database Management<\/p>\n

    (4 CR)\u00a0<\/em><\/p>\n

    CSE 414<\/strong>: Introduction to Database Systems<\/td>\n

    		Fundamentals of relational modeling and query languages (SQL), relational algebra and optimizations, and database architecture.<\/td>\n<\/tr>\n
    BIOST = Biostatistics; CHEME = Chemical Engineering; CSE = Computer Science and Engineering<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

    Eligibility<\/h3>\n

    To declare the DSO, the doctoral student must meet the following requirements:<\/p>\n